Correlation vs. Causation

Front

What is the definition of correlation?

Back

Correlation is a statistical measure that describes the extent to which two variables change together. It can be positive, negative, or zero.

Front

What does a strong positive correlation indicate?

Back

A strong positive correlation indicates that as one variable increases, the other variable also tends to increase.

Front

What does a strong negative correlation indicate?

Back

A strong negative correlation indicates that as one variable increases, the other variable tends to decrease.

Front

What is the range of correlation coefficients?

Back

Correlation coefficients range from -1 to +1, where -1 indicates a perfect negative correlation, +1 indicates a perfect positive correlation, and 0 indicates no correlation.

Front

What does it mean if two variables have no correlation?

Back

If two variables have no correlation, it means that there is no predictable relationship between them, and changes in one variable do not affect the other.

Front

How is the correlation coefficient (r-value) interpreted?

Back

The r-value is interpreted as follows: 0.0-0.1 (no correlation), 0.1-0.3 (weak correlation), 0.3-0.5 (moderate correlation), 0.5-0.7 (strong correlation), 0.7-1.0 (very strong correlation).

Front

What is the difference between correlation and causation?

Back

Correlation indicates a relationship between two variables, while causation implies that one variable directly affects the other.

Front

What is an example of a strong positive correlation?

Back

An example of a strong positive correlation is the relationship between height and weight in children; as height increases, weight tends to increase.

Front

What is an example of a strong negative correlation?

Back

An example of a strong negative correlation is the relationship between the amount of exercise and body weight; as exercise increases, body weight tends to decrease.

Front

What does an r-value of -0.89 indicate?

Back

An r-value of -0.89 indicates a strong negative correlation between the two variables.